Method of pupil segmentation

ABSTRACT

A method of pupil segmentation in a digital image of a vertebrate eye, said image being an intensity image composed of pixels having each a specific intensity value, the method comprising the steps of:
         deriving a texture image from the intensity image, said texture image being composed of pixels having each a specific contrast value;   forming a combined image by point-wise combining the intensity image with the texture image,   identifying a set of pixels in the combined image which fulfill a combined low-intensity and low-contrast criterion; and   approximating a boundary of said set by a convex curve and taking said convex curve as a boundary of the pupil.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to a method of pupil segmentation in a digitalimage of a vertebrate eye. The term “vertebrate” shall be understood toinclude humans.

2. Description of Background Art

A method of this type is used in the context of biometric identificationsystems based on iris analysis.

A biometric personal identification system based on iris analysis hasbeen described by John G. Daugman in U.S. Pat. No. 5,291,560 and in J.G. Daugman: “How Iris Recognition Works”, IEEE Transactions on Circuitsand Systems for Video Technology, Vol. 14, no. 1, pp. 21-30, January2004.

Such identification systems take advantage of the fact that the iris ofthe eye of an individual, which may be a human or a vertebrate, has acharacteristic pattern that is unique for that particular individual, sothat an iris analysis may be used for uniquely identifying theindividual. To that end, an image of an eye of the individual iscaptured with a digital camera, and image processing algorithms are usedfor recognizing the pupil and the iris of the eye in the digital image.Then, the iris pattern is normalized so as to compensate the effect ofvarying dilation or contraction of pupil, and a filter procedure isemployed for transforming the normalized image of the iris into adigital code, a so-called iris code, that is unique to the individualand may therefore be used for identification purposes.

Once an iris code of an individual has been created and stored, thatindividual may be identified by capturing again an image of its eye,creating an iris code on the basis of the new image, and checking theiris code thus obtained against the code that had been storedpreviously.

EP 0 821 912 B1 describes a pupil segmentation method that is based onthe consideration that the pupil of an eye is an area that has a lowdensity and low contrast. The pupil region in the image is thereforedetermined by extracting a region having a surface area within a rangeof predetermined threshold values. The result is a pupil area which hasan irregularly shaped boundary. This boundary is then approximated by anellipse which is considered as the boundary of the pupil.

US 2009/0208064 A1 describes a pupil segmentation method wherein a pupilregion which may have an irregular shape is determined by extracting aregion having an intensity that is lower than a certain threshold andthen computing the convex hull of that region.

SUMMARY OF THE INVENTION

It is an object of the invention to provide a pupil segmentation methodthat can appropriately describe pupil shapes that deviate significantlyfrom a circular or elliptic shape.

This object is achieved with a method of pupil segmentation in a digitalimage of a vertebrate eye, said image being an intensity image composedof pixels having each a specific intensity value, the method comprisingthe steps of:

-   -   deriving a texture image from the intensity image, said texture        image being composed of pixels having each a specific contrast        value;    -   forming a combined image by point-wise combining the intensity        image with the texture image,    -   identifying a set of pixels in the combined image which fulfill        a combined low-intensity and low-contrast criterion; and    -   approximating a boundary of said set by a convex curve and        taking said convex curve as a boundary of the pupil.

The combined low-intensity and low-contrast criterion can characterizethe pupil area with high selectivity, so that the pupil area can bedelimited from other areas in the image, regardless of the shape of thepupil area.

In this context, “point-wise” means that, for each point of the image,the intensity value is combined with the contrast value of the samepoint. This does not necessarily mean that the pixel compositions of theintensity image and the contrast image must be identical. For example, apixel of the contrast image may cover several, e.g. four, pixels of theintensity image. Then, the point-wise combination would result in fourpixels of the product image, each pixel corresponding to one of thepixels of the texture image and to which is assigned a combination ofthe contrast value of that pixel and a common intensity value of allfour pixels.

In a preferred embodiment, the combined image is a product image formedby point-wise multiplication of the intensity image with the textureimage, and the combined low-intensity and low-contrast criterion is athreshold criterion for the product image.

In an alternative embodiment, there could be two different thresholds,one for the intensity image and one for the texture image, and thecombined criterion could consist of deciding that a point is part of thepupil if both the intensity and texture values fall below theirrespective thresholds. That is, there could be a logical AND of twoindependent tests of the two properties rather than a test of thecombined measure. It would also be possible construct a combined measurewith a binary operation other than a product. For example, one could addthe two measures, or one could apply compressive or acceleratingnonlinearities, or thresholds or clipping to the values before addingthem. In the case where these nonlinearities correspond to a logarithmicfunction the result would be essentially equivalent to the embodimentusing the product image. Thus, it is in fact possible to construct acontinuum of ways of combining the two measures that has the product asa special case. These alternative embodiments may be useful in isituations in which it is desired to de-emphasize the texture (orintensity) measure to obtain better performance.

In general, the set of pixels obtained by checking the combinedlow-intensity and low-contrast criterion will be a non-convex set whichmay have a ragged boundary. The step of approximating that boundary by aconvex curve results in a boundary area having a smoother and, inparticular, convex contour which is very suitable for the further stepsin the process of creating an iris code and is nevertheless capable ofdescribing a pupil shape that deviates significantly from a circular orelliptic shape.

While a circle can be fully described by three independent parameters,i.e. the radius and the x and y coordinates of the center, and anellipse can be fully described by five independent parameters (thecoordinates of the two focal points and the dimension of the largeaxis), the method according to the invention permits to describe theboundary of the pupil as a geometrical line object that can only befully described by more than five independent parameters. For example,the pupil boundary may be described by a polygon that has such a largenumber of vertices that it approximates a smooth closed line. In thiscase, the independent parameters necessary for describing the lineobject are the coordinates of each of the vertices. Other examples ofpossible boundary descriptions are Bezier curves, or B-spline curves.

In a preferred embodiment, the convex curve describing the pupilboundary is a convex hull of the set of pixels. This provides a simpleway of minimizing the effects of granula iridica and, in extreme cases,of creating a single encompassing pupil region when the low-levelsegmentation produces disconnected areas.

As an alternative, something similar could be accomplished by shrinkingan egg or almond shaped parametric curve until it could not be shrunkfurther without sacrificing some pupil pixels. Curves of this type canbe represented by polynomial curves of order three or above or by unionsof circular arcs of varying radius. This parametric approach might, infact, be a preferred technique when dealing with a vertebrate such as agoat or a cat which has a more regular pupil than a horse but not onethat can be modeled as a circle or ellipse.

In general, identification systems based on iris analysis have to copewith the problem that, when the image of the eye is taken, the iris willnormally be distorted to some extent, because the pupil that issurrounded by the iris dilates and contracts dependent on theillumination conditions. It is therefore required for an efficient irisanalysis that the image of the iris is normalized in order to eliminatethe distortions caused by the pupil dilation and contraction. In knowniris analysis methods for individuals that have circular pupils, as mosthumans, the normalization can be achieved by using polar coordinates andnormalizing the radius coordinate to the difference between the radii ofthe circular outer and inner boundaries of the iris. The segmentationmethod according to the invention has the advantage that it provides adescription of a pupil contour that can be used for constructing acoordinate system permitting to normalize the iris image even when thepupil contour, i.e. the inner boundary of the iris, is neither circularnor elliptic.

It is therefore another object of the invention to provide a method ofcreating a binary iris code based on a normalization of the iris imagewhich, by using the segmentation method described above, is more robustagainst irregular pupil shapes.

Further objects and features of the invention will become clear as thedescription proceeds.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from thedetailed description given hereinbelow and the accompanying drawingswhich are given by way of illustration only and thus are not limitativeof the present invention, and wherein:

FIG. 1 is a schematic front view of an eye of a vertebrate;

FIG. 2 is an overall flow diagram of a method for creating an iris codefrom a captured image of the eye;

FIGS. 3 and 4 are top plan views of the eye and a camera for taking animage of the eye;

FIG. 5 is a diagram illustrating a relation between a camera-to-eyedistance and a spacing of specularity spots;

FIG. 6 is a top plan view similar to FIGS. 3 and 4 for an oblique aspectangle of the camera;

FIG. 7 is a flow diagram of an image capturing method;

FIG. 8 is a flow diagram of a method according to the invention forsegmenting a pupil in the image of the eye;

FIG. 9 is an example of a histogram used in the method that has beenillustrated in FIG. 8,

FIG. 10 is an example of a set of pixels obtained in the pupilsegmentation process;

FIG. 11 illustrates the contour of the pupil obtained as a result of thesegmentation process;

FIG. 12 is a diagram illustrating a first embodiment of a method fornormalizing an image of the iris of the eye;

FIG. 13 is a diagram illustrating another embodiment of the method fornormalizing the iris image;

FIGS. 14 and 15 illustrate examples of normalized iris images;

FIG. 16 is a block diagram of an identification system for horses, basedon iris analysis; and

FIG. 17 shows an example of a record in a database used in theidentification system shown in FIG. 16.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As an example of an image of an eye of a vertebrate, FIG. 1 shows animage of a (left) eye 10 of a horse. An upper lid 12, a lower lid 14, aconjunctiva 14, a pupil 18 and an iris 20 of the eye have been shownschematically. As is typical for horses, the pupil 18 is elongated inlateral direction. A bright sclera 22 of the eye is obscured almostcompletely by the eyelids, so that only small fractions of an outerboundary 24 of the iris (iris/sclera boundary) are visible.

FIG. 2 illustrates essential steps of a method for creating an iris codefrom an image of the type shown in FIG. 1.

In a step S1, an image of the eye is captured with a digital camera 26(shown in FIG. 3).

In step S2, the digital image is subjected to image processingalgorithms for delineating a boundary 28 of the pupil 18.

In step S3, the image is subjected to further image processing forcreating a normalized iris image, i.e. an image of the iris that isnormalized to a standard size of the outer iris boundary 24 and astandard size of the pupil 18. Such image normalization is necessarybecause, in practice, the pupil 18 dilates and contracts in response tovarying illumination conditions, so that, when two images of the sameiris are compared, the position and shape of the pupil boundary 28 willnormally not be the same.

Finally, in step S4, the normalized iris image is filtered with suitablefilter for obtaining a binary iris code which has a reasonable number ofbits and is nevertheless capable of encoding the characteristicstructural features of the iris 20.

As is shown in FIG. 3, the camera 26 that is used for capturing theimage of the eye 10 in step S1 as a casing 30 and an object lens 32. Twolight sources 34 are rigidly mounted on the casing 30 and are disposedsymmetrically on both sides of the object lens 32. Preferably, the lightsources 34 emit light in the near infrared to which the camera 26 issensitive, so that the light sources provide good illuminationconditions without blinding the horse and causing the pupil 18 tocontract excessively. Near infrared illumination is used also to moreeffectively reveal iris structure because it penetrates iris pigmentbetter than visible wavelengths.

As has schematically been shown in FIG. 3, ingoing light rays 36propagate from each of the light sources 34 to the eye 10 and outgoingrays 38 propagate from the eye 10 to the object lens 32 after havingbeen reflected at a cornea 40 of the eye.

As a result, the image captured with the camera 26 includes aspecularity pattern formed by two bright specular spots 42 (see alsoFIG. 1), which are reduced mirror images of the two light sources 34.Although the light sources 34 have identical sizes and brightnesses, thespots 42 may be different in size, dependent upon the local curvature ofthe cornea 40.

The positions of the specularity spots 42 in the image are determined bythe positions of the light sources 34 relative to the camera 26 (FIG. 3)and by the position of the camera 26 relative to the eye 10, inaccordance with the laws of geometrical optics. The ingoing ray 36 fromeach light source and the corresponding outgoing ray 38 are symmetricwith respect to the axis of incidence, i.e. the normal to the cornea 40at the reflection point of the rays 36, 38.

More specifically, as has been illustrated in FIG. 4, the spacing sbetween the two spots 42 becomes smaller when the distance D between thecamera 26 and the eye 10 becomes larger. Thus, since the distances ofthe light sources 34 from the object lens 32 are known, it is possibleto calculate the camera-to-eye-distance from the spacing between thespecularity spots 42 when the shape of the cornea 40 is known.

In a first approximation, the cornea 40 can be assumed to be sphericalwith a constant curvature that will be known for a given species, atleast for adult individuals. For non-adults, there will be a knownrelation between the curvature of the cornea 40 and the age of theindividual. In this first approximation, the camera-to-eye-distance Dcan be calculated on the basis of straightforward geometricalconsiderations.

In a more realistic approach, the local curvature of the cornea 40 willbe a function of the distance of the respective incidence point from thecenter of the pupil 18. Then, for a given configuration of the camera 26and the light sources 34 and a given animal species, the relationbetween the spacing s of the specular spots 42 and thecamera-to-eye-distance D will be given by a function that can bedetermined empirically. An example of a graph 44 of such a function hasbeen shown in a diagram in FIG. 5, where the specularity spacing s, asmeasured in units of pixels in the digital image, is indicated on theabscissa, and the camera-to-eye-distance D is indicated on the ordinate.

For certain species, e.g. for horses, the cornea typically has different(but constant) curvatures in the vertical and horizontal direction.Then, the assumption of a spherical cornea is realistic as long as thespecularity spots 42 are separatated along a roughly horizontal section.

In a processor for processing the digital image obtained with the camera26 (the processor may be implemented in the camera 26 and has not beenshown here), the function represented by the graph 44 may be stored inthe form of a look-up table or in the form of an algebraic expression,so that the camera-to-eye-distance D can readily be derived from thespacing s between the spots 42.

FIG. 6 illustrates a situation in which the camera 26 captures the imageof the eye 10 from a slightly inclined position, so that an aspect axisA that passes through the eye 10 and the camera 26 forms an angle α(aspect angle) with a line of sight S of the eye 10, the line of sight Sbeing defined as a line that passes through the center of the pupil 18and is normal to the plane of that pupil. In this case, one of thespecular spots 42 is closer to the line of sight S and hence to thecenter of the pupil 18 than the other spot.

In practice, the optical axis A of the camera 26 may deviate from theline of sight S of the eye 10 in both, horizontal direction (as shown inFIG. 6) and vertical direction (not shown). In FIG. 1, a common centeror mid-point of the spots 42 has been designated as 46. A deviation ofthe camera 26 from the line of sight S in horizontal directiontranslates into a horizontal offset a of the mid-point 46 from acentroid or center 48 of the pupil 18, and a deviation of the camerafrom the line of sight S in vertical direction translates into avertical offset b.

When the camera-to-eye-distance D is known, the aspect angles such asthe angle α can be calculated from the offsets a and b. In order tomeasure these offsets in the digital image, the position of the center48 of the pupil 18 must be known. To that end, as is generally known inthe art of image processing and has been illustrated in FIG. 1, arectangular bounding box 50 may be drawn around the relatively dark areaof the pupil 18, and the center of that bounding box 50 may be taken asthe center 48 of the pupil.

Thus, in summary, an analysis of the specularity pattern (spots 42)permits to determine four of the six degrees of freedom of the positionof the camera 26 relative to the eye 10. The two remaining degrees offreedom are rotations of the camera about a horizontal and a verticalaxis, respectively, which, however, lead only to a shift of the image inhorizontal and vertical direction in FIG. 1 and can easily becompensated by moving the image such that the center 48 of the pupilwill be in the center of the image. Knowledge of the four relevantdegrees of freedom of the camera 26 permits to check the camera positionat the very instant when the image of the eye is captured.

An example of a method of image capturing utilizing the principles thathave been described above will now be explained in conjunction with aflow diagram shown in FIG. 7. This flow diagram expands the step S1 inFIG. 2.

In step S1-1, the image of the eye 10 is “recorded” with the camera 26.This means that the camera is operating in a video mode in which theimage data are continuously transmitted from a CCD array of the camerato a processing module where they are processed further. Simultaneously,the image may continuously be displayed on a display of the camera, butthe image data are not yet saved in a memory as long as the user of thecamera does not actuate a trigger button of the camera.

In step S1-2, the processing software searches for the specularitypattern, i.e. for the bright spots 42, in the digital image. These spotswill have the highest intensity values in the entire image, so that theyare easy to find and can then serve as a guide indicating the image areawhere the pupil (18) of the eye should be expected.

In step S1-3, the distance between the spots 42 is measured (in units ofpixels), and the camera-to-eye-distance D is calculated on the basis ofa function of the type shown in FIG. 5.

In step S1-4, the processing algorithm searches a region with very lowintensity values in the vicinity of the spots 42. This region is acandidate for the pupil 18 and is enclosed in the bounding box 50. Thisprovides at least a rough estimate for the position of the center 48 ofthe pupil.

Then, in step S1-5, the offsets a and b (FIG. 1) are measured and theaspect angles (the angle α and its counterpart for a vertical offset)are calculated on the basis of the camera-to-eye-distance D that hasbeen calculated before in step S1-3.

Optionally, the angular offsets may be used for refining the calculationof the camera-to-eye-distance D. When the cornea 40 is not spherical,the exact shape of the graph 44 in FIG. 5 will also depend upon theangular offsets, so that an appropriate function for deriving thedistance D from the specularity spacing should be selected dependentupon the actual values of the angular offsets.

In step S1-6, the calculated distance D and the angular offsets arechecked against predetermined tolerance ranges. When the calculatedvalues are within their respective tolerance ranges (Y), this means thatthe camera is in a suitable (or almost optimal) position for capturingthe image of the eye 10. Then, the camera is unlocked in step S1-7, anda corresponding “ok” sign is shown on the display of the camera,signaling to the user that he may push the trigger button. When thecamera is triggered, in step S1-8, the image is scaled to a standardsize on the basis of the calculated distance D, so that eyes of equalsize will also have the same size in the digital image, regardless ofthe exact camera-to-eye-distance. Further, the image is clipped to astandard height and width which are selected such that the image willcontain only the relevant image part, i.e. the region around the iris20. Finally, the scaled and clipped image is saved in a memory forfurther processing.

When it is found in step S1-6 that at least one of the distance D andthe angular offsets is not admissible (N), corresponding correctioninstructions are shown on the camera display in step S1-9, showing theuser how he has to move the camera in order to bring the distance andthe angular offsets into the respective tolerance ranges. From stepS1-9, the program loops back to step S3, and the steps S1-3 to S1-6 arerepeated until a suitable camera position has been reached.

In a modified embodiment of this method, calculations corresponding tothose in steps S1-3 to S1-5 will be performed only after the camera hasbeen triggered and the image has been saved. Depending on the result ofa check that is comparable to step S1-6, the user will then be advisedwhether the image is accepted or rejected. In this case, the processingdoes not have to be done in the camera but may done in a multi-purposecomputer to which the camera 26 is connected after the image has beentaken.

FIG. 8 is a flow diagram expanding the step S2 (FIG. 2) of delineatingthe pupil boundary 28 (FIG. 1).

In this example, the image that has been saved in step S1-8 is amonochromatic (infrared) intensity image, wherein an intensity value isassigned to each pixel of the image. Of course, a straightforwardextension to color images with, for example, three intensity values perpixel would be possible.

In the intensity image, the area of the pupil 18 is a low intensity areawhich, however, is “pierced” by the specularity spots 42. In order toreconstruct the original pupil area, the specularity pattern is removedin step S2-1. This can be achieved by means of an inpainting algorithmwhich fills in the area of the spots 42 using information from thesurrounding area of the pupil 18. Mathematically, the problem isequivalent to solving a system of Navier-Stokes equations in classicalfluid dynamics, when the image intensity is considered as a “streamfunction” for a two-dimensional incompressible flow. The Laplacian ofthe image intensity plays the role of the vorticity of the fluid and istransported into the region of the spots 42 by a vector field defined bythe stream function. Details of the algorithm are described byBertalmio, M.; Bertozzi, A. L.; Sapiro, G.: “Navier-Stokes, FluidDynamics and Image and Video Inpainting” Computer Vision and PatternRecognition, 2001, CVPR 2001, Proceedings of the 2001 IEEE ComputerSociety Conference, vol. 1, pp. 1-355-1-362. Of course, any other knowninpainting method may used.

As the pupil 18 is a low intensity area, the intensities are compared toa threshold in step S2-2, and pixels having an intensity above thethreshold are masked-off, so that only low intensity areas remain whichshould include the pupil area. The threshold value may for example be25% of the highest intensity value (after the specularity spots 42 havebeen eliminated).

The pupil area is not only characterized by low intensity values butalso a low texture area, i.e. an area that is almost free of substantialhigh-frequency intensity variations. This is why, in addition to theintensity criterion, the method for identifying the pupil area uses alsoa texture image that is constructed in step S2-3.

The texture image can be derived from the intensity image by computingan edge contrast for each pixel. For example, a Sobel first derivativefilter may be used for that purpose, and the output may be smoothenedwith a spatial averaging filter.

Step S2-4 is a step of forming a pointwise product of the maskedintensity image obtained in step S2-2 and the texture image obtained instep S2-3, i.e. the intensity value of each pixel is multiplied by thecontrast value of that pixel. Thus, image areas in the masked intensityimage which have both, a low intensity and low texture are characterizedby particularly low values in the product image. The pixels of the pupilarea can be expected to have the lowest product values in the entireimage, and, moreover, these product values will be almost identical,since the pupil area is essentially uniform in both, intensity andtexture. Consequently, a good estimate for the real pupil area will beobtained by comparing the product values to a suitably selectedthreshold.

To that end, a histogram of the product image is computed in step S2-5.An example of such a histogram has been illustrated in FIG. 9, showingthe frequency of occurrence of each product value (i.e. the number ofpixels having that product value) as a function of the product value.The pixels of the pupil 18 will form a distinct peak 52 at the low endof the histogram.

In step S2-6, a threshold T (FIG. 9) is selected such that it marks thehigh end of the peak 52. Consequently, all the pixels having productvalues below the threshold T can be expected to form the pupil area.

The set of pixels that fulfils this threshold criterion is selected instep S2-7. An example of such a set 54 has been shown in FIG. 10. Inpractice, there may of course be outliers, e.g. “islands” 56 of pixelswhich are located outside of the area of the pupil 18 but have productvalues below the threshold T, and “bays” 58 and “lakes” 60 of pixelsinside the pupil area which have product values above the threshold T.

In step S2-8, the islands 56 are eliminated, using well known imageprocessing techniques such as morphological dilation and erosionoperations. The bays 58 and lakes 60 are eliminated by forming theconvex hull of the remaining set 54, as has been shown in FIG. 11.Normally, this convex hull will be a good guess for the real boundary 28of the pupil 18.

There may however be cases where the pupil area obtained in this way isunrealistically or unacceptably large or unrealistically or unacceptablysmall. Reasons for this may be for example an inappropriate choice ofthe threshold T or an extreme dilation or contraction of the pupil ofthe horse as a result of abnormal illumination conditions. The lattercase will not actually be “unrealistic”, but should nevertheless beexcluded, because extreme dilations or contractions of the pupil wouldimply that the iris 20 is distorted to an extent that makes the match ofiris codes impossible even for images taken from the same individual.For this reason, it is checked in step S2-9 whether the dimensions ofthe iris 18, e.g. the total height, as determined on the basis of theconvex hull is within an acceptable range. The upper and lower limits ofthis range can be determined empirically for the given species ofvertebrates. It should be noted in this context that the height of thepupil area in the digital image can be taken as a measure for the actualheight of the pupil because the image capturing process (step S1)assures that the images are always taken from approximately the samedistance and remaining distance variations have been compensated byimage scaling.

If the result of the check in step S2-9 is negative (N), it is checkedin step S2-10 whether a flag has been set. If the flag has not been set(N), the flag is set in step S2-11, and the program loops back to stepS2-6. It will then be attempted in step S2-6 to obtain a pupil withreasonable size by correcting the threshold T. When the dimension of thepupil was too large, the threshold T will be shifted to lower values,and it will be shifted to higher values when the dimension of the pupilwas too small. When the steps S2-6 to S2-8 have been run-through asecond time, and it is still found in step S2-9 that the dimensions ofthe pupil are not acceptable, it is found in step S2-10 that the flag isset (Y) which indicates that an attempt to correct the threshold T hasalready been made and has failed. In this case, the image is rejected instep S2-12 and the user is invited to capture a new image.

Of course, in a modified embodiment, the flag checked in step S2-10 maybe replaced by a count value counting the number of times that the loopS2-6 to S2-11 has been run-through, so that a larger number ofcorrection attempts for the threshold T can be made.

When the check in step S3-9 has the result that the dimensions of thepupil are acceptable (Y), the process of delineating the pupil boundaryis completed, and the program continues with in step S2-13 for callingthe procedure for normalizing the iris image (step S3).

In a modified embodiment, the calculation the angular offsets (as instep S1-5 in FIG. 7) or a more exact calculation of these offsets may beperformed subsequent to the delineation of the pupil boundary so thatthe more exact pupil boundary may be used for determining the positionof the center 48 of the pupil.

As is shown in FIG. 11, the iris 20 is a ring shaped area that isdelimited at the inner periphery by the pupil boundary 28 and at theouter periphery by the iris/sclera boundary 24. When the pupil 18dilates or contracts while the outer boundary 24 of the iris remainsunchanged, each point of the iris 20 will be shifted in a generallyradial direction by an amount that is large when the point is close tothe pupil boundary 28 and becomes smaller when the point is closer tothe stationary outer boundary 24. The purpose of the normalization stepS3 is to obtain a normalized iris image that is not affected by thedilations and contractions of the pupil 18, so that the iris images andthe resulting iris codes can be compared to one another.

The approach is to transform the coordinates of the pixel of the irisarea from a Cartesian xy-coordinate system (shown in FIG. 11) into acoordinate system that may be called a “generalized polar coordinatesystem”.

If the pupil 18 could be assumed to be circular, a true polar coordinatesystem could be used, as is known for iris analysis for humans. In thatcase, normalization for varying pupil radii could easily be achieved bysetting the radius coordinate of the pixel in proportion to thedifference between the radius of the iris/sclera boundary and the radiusof the pupil. However, in the method that is discussed here, the pupilboundary 28 is not defined as a circle but as a more complex geometricalline object which can only be described by a larger number ofparameters. for example, the boundary 28 may be polygon with a verylarge number of corners connected by straight line segments which willapproximate the smooth contour of the true boundary. Such object canonly be described by defining the coordinate positions of each vertex ofthe polygon. This makes the identification system more robust againstirregular pupil shapes but, on the other hand, requires a non-trivialcoordinate transformation for normalization purposes.

In order to obtain a suitable new coordinate system, the ring-shapedarea of the iris 20 is divided into a sequence of radial spokes 62, ashas symbolically been shown in FIG. 12. In practice, the number ofspokes will be significantly larger than in FIG. 12, preferably so largethat each pixel of the image lies on one of the spokes 62. This mayinvolve a re-dimensioning and re-shaping of the pixels. For at leastsome of the spokes 62, the spacing from spoke to spoke is larger nearthe outer boundary 24 than near the pupil boundary 28. Near the outerboundary 24, larger pixels may be formed by sampling or averaging over aplurality of pixels of the original image, which means a loss ofresolution. Conversely, when the original pixel size is approximatelyretained near the outer boundary 24, two or more of the spokes 62 maypass over the same original pixel near the pupil boundary 28, whichmeans that the image information becomes redundant. The number anddensity of the spokes 62 is therefore selected to be a reasonablecompromise between resolution and redundancy.

The spokes 62 are numbered sequentially along the boundary of the pupil28. In the simplified example in FIG. 12 (20 spokes), the spoke numbersare running from 1 through 20. For a pixel located on a given spoke 62,the running number of that spoke will be the first coordinate “a” in thenew coordinate system. As an example, FIG. 12 shows a pixel 64 on spokenumber 13, so that the first coordinate “a” of this pixel will be a=13.A second coordinate r of the pixel 64 is determined by its location onthe spoke, normalized to the entire length of the corresponding spoke,so that a pixel located on the pupil boundary 28 would have the secondcoordinate r=0 and a pixel located on the outer boundary 24 of the iriswould have the coordinate r=1.

Thus, when the pupil boundary 28 is dilated while the (a, r)-coordinatesare kept constant, the pixel 64 designated by these coordinates willmove outwardly along its spoke. This movement of the pixel will at leastapproximately simulate the movement of the corresponding point on theiris when the iris is physically distorted by the dilation of the pupil.

Thus, when two images of the same iris are captured for differentdilation states of pupil 18, pixels having the same (a, r)-coordinateswill at least approximately have the same intensity values.

In the scheme that has been described above, there is still some choicein the exact arrangement of the spokes 62. In the example shown, thespokes are arranged to be normal to the pupil boundary 28 (i.e. normalto the tangent to this boundary at the point where the spoke meets theboundary).

In a modified embodiment, a number of equidistant points may be definedon the pupil boundary 28, and a like number of equidistant points mightbe defined on the outer boundary 24, and the spokes may be formed byconnecting corresponding points on the two boundaries.

Other arrangement of the spokes 62 are also possible as long as the wayhow the pixels are shifted in response to dilations and contractions ofthe pupil correspond with sufficient accuracy to the actual distortionof the iris.

When the iris/sclera boundary 24 or at least a substantial part thereofis visible in the captured image, so that any obscured parts of theboundary can readily be interpolated on the basis of the assumption thatthe boundary has a regular (e.g. circular) shape (as is typically thecase for humans), the length of each spoke 62 is determined by therequirement that the outer end of the spoke must be located on theboundary 24. However, when the boundary 24 is largely obscured as in theexample shown in FIG. 1, a suitable way must be found to “reconstruct”the boundary 24 or, more precisely, to construct an imaginary boundarythat will be similar to the real boundary.

In the example shown in FIG. 12, it is assumed that the height of theiris 20, i.e. the distance between the lower apex and the upper apex ofthe boundary 24, is essentially the same for all adult individuals ofthe same species. For horses, it has been confirmed that this assumptionis fulfilled with sufficient accuracy.

Since the image that has been obtained in step S1-8 is scaled to astandard camera-to-eye-distance D, the above assumption implies that thevertical dimension of the boundary 24 has a fixed value, e.g. 150 pixelin the x-y coordinate system shown in FIG. 11. Then, a reasonable valuefor the length of the spokes 62 can be obtained by subtracting thevertical dimension of the pupil 18 (measured in pixels in FIG. 11) fromthe vertical dimension of the boundary 24 and dividing the difference bytwo. In the example shown in FIG. 12, all the spokes 62 are assumed tohave the same length which is calculated pursuant to this formula.Together with the requirement that the spokes are normal to the pupilboundary 28, this defines the shape of the outer boundary 24.

At least for horses, this way of constructing the boundary 24 isrealistic. When the dilation and contraction of the pupil 18 is notisotropic, the horizontal dimension of the boundary 24 (distance fromthe left apex to the right apex in FIG. 12) will not be exactlyconstant. However, the resulting deviations from the physical shape ofthe iris are found to be acceptable.

FIG. 13 illustrates a modified embodiment wherein it is assumed that notonly the height but also the width of the pupil 20 (its outer boundary24) is constant, and the shape of the boundary 24 can be approximated bya suitable oval, such as an ellipse or a Cassini oval. In the exampleshown in FIG. 13, the height of the iris is assumed to be 150 pixel andthe width is assumed to be 250 pixel. Again, the spokes 62 are arrangedto be normal to the pupil boundary 28. Further, in this example, thespokes 62 have been arranged such that their outer end points (on theboundary 24) are equidistant. As an alternative, the inner end points(on the pupil boundary 28) may be made equidistant (as in FIG. 12), orequal distances may be required for corresponding points on the spokes62 for any given coordinate value r between 0 and 1.

FIG. 14 gives an example of a normalized iris image 66 resulting fromthe coordinate trans-formation that has been explained in conjunctionwith FIG. 12. Here, the (a, r)-coordinates are represented as Cartesiancoordinates, with the a-coordinates on the abscissa and ther-coordinates on the ordinate, so that the ring-shaped area of the pupil20 is evolved into a rectangular strip 68.

Ideally, two normalized iris images 66 obtained from two photographs ofthe same eye should be identical. In practice, however, the absoluteposition of, e.g., spoke number 1 on the pupil boundary 28 is somewhatarbitrary, so that the a-coordinates in one image may be slightlyshifted relative the a-coordinates in the other image. This effect maybe compensated by a slight “rotation” of the spoke pattern along theboundary 28 or, equivalently, a cyclic permutation of the spokes in thenormalized image 66, as has been illustrated in FIG. 15.

Such a shift of the a-coordinates might also be caused by a slightrotation of the camera 26 about the aspect axis A (FIG. 6) when theimage is captured. In this case, an alternative possibility tocompensate the shift is to rotate the digital image (prior to thecoordinate transformation) until the axis of largest elongation of thepupil is horizontal in the image.

Another type of differences between two normalized images taken from thesame eye may be caused by different positions of the upper and lowereyelids 12, 14 at the instant when the photos are taken, so that smalleror larger parts of the iris 20 are obscured. Consequently, whencomparing two normalized images, the areas of the iris that may beaffected by slight movements of the eyelids should be ignored. In FIG.12, these areas have been delimited by straight lines 70, 72 in the topand bottom parts of the iris 20. In FIG. 14, the ignored parts of thestrip 68 in the normalized image 66 are delineated by correspondinglines 70′, 72′.

In order to facilitate and speed up the identification of individuals,the normalized images 66 are not directly compared to one another, butare subjected to a filter procedure resulting in a more compressed iriscode (step S4 in FIG. 2). Such filtering procedures are known in theart. Examples are described by Daugman in U.S. Pat. No. 5,291,560 and inthe article “How Iris Recognition Works” that has been cited in theintroductory part of the present description. In brief, the process mayinclude filtering horizontal slices through the normalized iris image 66with logarithmic Gabor filters. This filter process provides a complexnumber of each point along the slice. The phases of these complexnumbers may then be quantized into a two-bit representation specifyingthe quadrant in the complex plane in which the phase angle lies. Thesebits, together, form the iris code.

In order to identify an individual, the iris codes thus obtained arematched to one another. Preferably, the code match is repeated forseveral “rotated” versions of the normalized image 66 (FIG. 15), and theportions delineated by the lines 70′, 72′ in FIG. 14 are excluded fromthe code match.

The code match basically comprises computing the number of non-matchingbits (Hamming Distance) by subjecting the two codes to be matched to anexclusive OR logical operation (XOR). The fraction of the non-matchingbits in relation to the total number of bits indicates the probabilitythat the two codes represent different eyes. When this fraction issmaller than a certain threshold, the code match is considered to havebeen successful.

FIG. 16 is a block diagram of an example of an identification systememploying the method according to the invention and designed foridentifying horses. A provider 74 of the identification servicesmaintains a database 76 and communicates with a number of clients 78 viaa communication network, e.g. the Internet.

Each client 78 can rent or purchase an iris code capturing kit 80 fromthe provider 74. Each kit 80 includes the camera 26 as well as thesoftware and hardware necessary for performing the method that has beendescribed above.

An example of a record 82 that is kept in the database 76 and describesan individual horse has been given in FIG. 17. The record 82 includes aniris code field 84 and several other fields for other relevant dataabout the horse, such as its name, race, color, and the like.

When a client 78 wants to register a horse in the database 76, he useshis kit 80 for capturing an image of one or both eyes of the horse andsends the iris code that has been created on the basis of this image tothe database 76 along with other information to be entered into therecord 82. Preferably, the client generates a plurality of iris codesfor the same eye of the horse, and a software in the kit 80 or in thepremises of the provider 74 checks whether these codes are consistent,i.e. whether code matches between these codes are successful. When acode match is not successful, another attempt may be made after havingperformed a cyclic shift of the bits in one of the codes to be compared,this bit shift reflecting a cyclic permutation of the spokes ora-coordinates as has been described in conjunction with FIGS. 14 and 15.When a sufficient number of iris codes has been obtained that canmutually be matched to one another, one of these codes or an averageover all these codes may be transmitted to the database 76 to be enteredinto the iris code field 84.

When a client 78 wants to retrieve data about an individual horse fromthe database 76, he uses the kit 80 for creating an iris code for thishorse and sends the iris code to the database 76, if possible togetherwith other information for identifying the horse, such as the name, theowner, and the like. In order to find the pertinent record in thedatabase, it may be more expedient to search for the name or other IDinformation rather than searching for a matching iris code. When apertinent record has been found, the iris code sent by the client 78 maybe checked against the iris code stored in the record in order toconfirm the identity of the horse. Again, the code match may be repeatedfor a bit-shifted version of the code if the first attempt was notsuccessful. Moreover, in order to improve the robustness of theidentification, it is possible that the client 78 captures a pluralityof images of the same eye of the same horse and sends the correspondingiris codes to the database 76, where they are matched against the storedcode. Then, identity will be confirmed when at least one of the codesmatches. The required number of codes may however be smaller than in thecase that a new record is entered into the database.

When a client 78 tries to find out the identity of an unknown horse, hemay send the iris code of that horse to the database 76, and this codemay be checked against the codes that are stored in all the records 82in the database, in order to determine the identity of the horse.

1. A method of pupil segmentation in a digital image of a vertebrateeye, said image being an intensity image composed of pixels having eacha specific intensity value, the method comprising the steps of: derivinga texture image from the intensity image, said texture image beingcomposed of pixels having each a specific contrast value; forming acombined image by point-wise combining the intensity image with thetexture image, identifying a set of pixels in the combined image whichfulfill a combined low-intensity and low-contrast criterion; andapproximating a boundary of said set by a convex curve and taking saidconvex curve as a boundary of the pupil.
 2. The method according toclaim 1, wherein said convex curve is a convex hull of said set ofpixels.
 3. The method according to claim 1, wherein said combined imageis a product image formed by point-wise multiplication of the intensityimage with the texture image, and said combined low-intensity andlow-contrast criterion is a threshold criterion for the product image.4. The method according to claim 3, wherein a threshold for saidthreshold criterion is determined individually for the digital image,based on a histogram analysis of the product image.
 5. The methodaccording to claim 4, wherein the histogram analysis comprisesidentifying a peak at a lower end of a range of product values appearingin the product image, and the threshold is selected to separate saidpeak from the rest of the histogram.
 6. The method according to claim 5,wherein said histogram analysis comprises a filtering operation forsmoothening the histogram, said filtering operation being performedbefore the peak is identified.
 7. The method according to claim 1,comprising a check whether a dimension of said boundary of the pupil, asdescribed by said convex curve, is within a predetermined range.
 8. Themethod according to claim 7, comprising a step of measuring a distancebetween the eye and a camera with which the digital image is captured,and a step of scaling the intensity image in proportion to the distancebetween the camera and the eye, such that dimensions of a detail in theimage of the eye indicate the corresponding true dimensions of thedetail in the real eye.
 9. The method according to claim 7, wherein thesegmentation process is aborted and the image is rejected when thedimension of the pupil boundary is not in said predetermined range. 10.The method according to claim 7, wherein, when said dimension is not inthe predetermined range, the steps of identifying a set of pixelsfulfill the combined low-intensity and low-contrast criterion and ofapproximating the boundary of said set by a convex curve are repeatedfor a combined low-intensity and low-contrast criterion.
 11. The methodaccording to claim 10, wherein the segmentation process is aborted andthe image is rejected when the dimension of the pupil boundary is not inthe predetermined range after the combined low-intensity andlow-contrast criterion has been modified a predetermined number oftimes.
 12. The method according to claim 1, for a digital image thatincludes a specularity pattern caused by reflection of at least onelight source at a cornea of the eye, the method comprising a step ofremoving said specularity pattern from the intensity image before theother steps of the segmentation are performed.
 13. The method accordingto claim 12, wherein an inpainting algorithm is used for removing thespecularity pattern.
 14. The method according claim 1, wherein saidconvex curve is a closed geometrical line object that can only bedescribed by more than five independent parameters.
 15. The methodaccording to claim 13, wherein said line object is a polygonapproximating a smooth closed curve.
 16. A method of creating a binaryiris code from image data of an eye of a vertebrate, including humans,using the method of claim 1 for pupil segmentation.
 17. The methodaccording to claim 16, comprising a step of transforming a digital imageof the iris into a coordinate system in which each point on the iris isdescribed by a first coordinate that indicates the position of the pointalong the boundary of the pupil and a second coordinate indicates thedistance of the point from said boundary.
 18. A data record stored on amachine readable data carrier, said data record including a binary iriscode obtained with the method according to claim
 17. 19. An iris codegeneration kit comprising a camera and software and hardware suitablefor carrying out the method according to claim
 1. 20. An identificationsystem for identifying vertebrates, including humans, on the basis of aniris code match, comprising at least one iris code generation kitaccording to claim 19 and a database storing data records according toclaim 18.